Learning Inverse Statics Models Efficiently
This work addresses efficient learning of inverse statics models for robotics, particularly in gravity compensation, but is incremental as it builds on existing methods.
The paper tackles the problem of learning inverse statics mappings for gravity compensation in robots by modifying Online Goal Babbling and Direction Sampling methods, achieving reductions in required samples by factors of at least 8 and 16 for 2R and 3R arms, respectively.
Online Goal Babbling and Direction Sampling are recently proposed methods for direct learning of inverse kinematics mappings from scratch even in high-dimensional sensorimotor spaces following the paradigm of "learning while behaving". To learn inverse statics mappings - primarily for gravity compensation - from scratch and without using any closed-loop controller, we modify and enhance the Online Goal Babbling and Direction Sampling schemes. Moreover, we exploit symmetries in the inverse statics mappings to drastically reduce the number of samples required for learning inverse statics models. Results for a 2R planar robot, a 3R simplified human arm, and a 4R humanoid robot arm clearly demonstrate that their inverse statics mappings can be learned successfully with our modified online Goal Babbling scheme. Furthermore, we show that the number of samples required for the 2R and 3R arms can be reduced by a factor of at least 8 and 16 resp. -depending on the number of discovered symmetries.